{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,26]],"date-time":"2026-06-26T14:06:06Z","timestamp":1782482766734,"version":"3.54.5"},"reference-count":48,"publisher":"Springer Science and Business Media LLC","issue":"31","license":[{"start":{"date-parts":[[2024,2,19]],"date-time":"2024-02-19T00:00:00Z","timestamp":1708300800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,2,19]],"date-time":"2024-02-19T00:00:00Z","timestamp":1708300800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"Nanjing International joint research and development project of China","award":["2022SX00001057"],"award-info":[{"award-number":["2022SX00001057"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["52078122"],"award-info":[{"award-number":["52078122"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimed Tools Appl"],"DOI":"10.1007\/s11042-024-18568-3","type":"journal-article","created":{"date-parts":[[2024,2,19]],"date-time":"2024-02-19T08:02:40Z","timestamp":1708329760000},"page":"76935-76952","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Toward enhancing concrete crack segmentation accuracy under complex scenarios: a novel modified U-Net network"],"prefix":"10.1007","volume":"83","author":[{"given":"Feng","family":"Qu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bokun","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qing","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fu","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yaojing","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2226-1618","authenticated-orcid":false,"given":"Caiqian","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,2,19]]},"reference":[{"key":"18568_CR1","doi-asserted-by":"publisher","first-page":"104316","DOI":"10.1016\/j.autcon.2022.104316","volume":"140","author":"EA Shamsabadi","year":"2022","unstructured":"Shamsabadi EA, Xu C, Rao AS, Nguyen T, Ngo T, Dias-da-Costa D (2022) Vision transformer-based autonomous crack detection on asphalt and concrete surfaces. Auto Constr 140:104316. https:\/\/doi.org\/10.1016\/j.autcon.2022.104316","journal-title":"Auto Constr"},{"issue":"5","key":"18568_CR2","doi-asserted-by":"publisher","first-page":"797","DOI":"10.1007\/s00138-009-0189-8","volume":"21","author":"T Yamaguchi","year":"2009","unstructured":"Yamaguchi T, Hashimoto S (2009) Fast crack detection method for large-size concrete surface images using percolation-based image processing. Inter Mach Visi Appli 21(5):797\u2013809. https:\/\/doi.org\/10.1007\/s00138-009-0189-8","journal-title":"Inter Mach Visi Appli"},{"key":"18568_CR3","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1016\/j.autcon.2018.07.008","volume":"94","author":"H Nhat-Duc","year":"2018","unstructured":"Nhat-Duc H, Nguyen Q-L, Tran V-D (2018) Automatic recognition of asphalt pavement cracks using metaheuristic optimized edge detection algorithms and convolution neural network. Auto Constr 94:203\u2013213. https:\/\/doi.org\/10.1016\/j.autcon.2018.07.008","journal-title":"Auto Constr"},{"key":"18568_CR4","doi-asserted-by":"publisher","first-page":"106","DOI":"10.1016\/j.cageo.2014.01.007","volume":"66","author":"A Arena","year":"2014","unstructured":"Arena A, Delle Piane C, Sarout J (2014) A new computational approach to cracks quantification from 2D image analysis: Application to micro-cracks description in rocks. Comp Geos 66:106\u2013120. https:\/\/doi.org\/10.1016\/j.cageo.2014.01.007","journal-title":"Comp Geos"},{"key":"18568_CR5","doi-asserted-by":"publisher","first-page":"1031","DOI":"10.1016\/j.conbuildmat.2018.08.011","volume":"186","author":"S Dorafshan","year":"2018","unstructured":"Dorafshan S, Thomas RJ, Maguire M (2018) Comparison of deep convolutional neural networks and edge detectors for image-based crack detection in concrete. Constr Build Mater 186:1031\u20131045. https:\/\/doi.org\/10.1016\/j.conbuildmat.2018.08.011","journal-title":"Constr Build Mater"},{"issue":"4","key":"18568_CR6","doi-asserted-by":"publisher","first-page":"255","DOI":"10.1061\/(ASCE)0887-3801(2003)17:4(255)","volume":"17","author":"I Abdel-Qader","year":"2003","unstructured":"Abdel-Qader I, Abudayyeh O, Kelly ME (2003) Analysis of Edge-Detection Techniques for Crack Identification in Bridges. J Compu Civ Eng 17(4):255\u2013263. https:\/\/doi.org\/10.1061\/(ASCE)0887-3801(2003)17:4(255)","journal-title":"J Compu Civ Eng"},{"key":"18568_CR7","doi-asserted-by":"publisher","first-page":"210","DOI":"10.1061\/(ASCE)0887-3801(2006)20:3(210)","volume":"20","author":"TC Hutchinson","year":"2006","unstructured":"Hutchinson TC, Chen Z (2006) Improved Image Analysis for Evaluating Concrete Damage. J Compu Civ Eng 20:210\u2013216. https:\/\/doi.org\/10.1061\/(ASCE)0887-3801(2006)20:3(210)","journal-title":"J Compu Civ Eng"},{"key":"18568_CR8","doi-asserted-by":"publisher","unstructured":"Zhao, H, Qin, G, Wang X (2010) Improvement of canny algorithm based on pavement edge detection. 2010 3rd International Congress on Image and Signal Processing. IEEE. https:\/\/doi.org\/10.1109\/CISP.2010.5646923","DOI":"10.1109\/CISP.2010.5646923"},{"key":"18568_CR9","doi-asserted-by":"publisher","unstructured":"Liu Y, Cho S, Jr B F S et al (2014) Automated assessment of cracks on concrete surfaces using adaptive digital image processing[J]. Smart Struct Syst 14(4):719\u2013741. https:\/\/doi.org\/10.12989\/sss.2014.14.4.719","DOI":"10.12989\/sss.2014.14.4.719"},{"issue":"1","key":"18568_CR10","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1061\/(asce)cp.1943-5487.0000446","volume":"30","author":"Y-F Liu","year":"2016","unstructured":"Liu Y-F, Cho S, Spencer BF, Fan J-S (2016) Concrete Crack Assessment Using Digital Image Processing and 3D Scene Reconstruction. J Compu Civ Eng 30(1):1\u201319. https:\/\/doi.org\/10.1061\/(asce)cp.1943-5487.0000446","journal-title":"J Compu Civ Eng"},{"key":"18568_CR11","doi-asserted-by":"publisher","unstructured":"Tanaka N, Uematsu K (1998) A crack detection method in road surface images using morphology[J]. IAPR MVA, Tokyo, 154\u2013157. https:\/\/doi.org\/10.2208\/jsceje.62.631","DOI":"10.2208\/jsceje.62.631"},{"issue":"1","key":"18568_CR12","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1016\/j.autcon.2005.02.007","volume":"15","author":"SK Sinha","year":"2006","unstructured":"Sinha SK, Fieguth PW (2006) Segmentation of buried concrete pipe images. Auto Constr 15(1):47\u201357. https:\/\/doi.org\/10.1016\/j.autcon.2005.02.007","journal-title":"Auto Constr"},{"issue":"1","key":"18568_CR13","doi-asserted-by":"publisher","first-page":"178","DOI":"10.1109\/tip.2005.860311","volume":"15","author":"I Giakoumis","year":"2006","unstructured":"Giakoumis I, Nikolaidis N, Pitas I (2006) Digital image processing techniques for the detection and removal of cracks in digitized paintings. IEEE Trans Image Process 15(1):178\u2013188. https:\/\/doi.org\/10.1109\/tip.2005.860311","journal-title":"IEEE Trans Image Process"},{"issue":"2","key":"18568_CR14","doi-asserted-by":"publisher","first-page":"247","DOI":"10.1134\/s1054661820020029","volume":"30","author":"N Aboutabit","year":"2020","unstructured":"Aboutabit N (2020) Reduced Featured Based Projective Integral for Road Cracks Detection and Classification. Pattern Recognit Image Anal 30(2):247\u2013255. https:\/\/doi.org\/10.1134\/s1054661820020029","journal-title":"Pattern Recognit Image Anal"},{"issue":"6","key":"18568_CR15","doi-asserted-by":"publisher","first-page":"1726","DOI":"10.1177\/1475921719896813","volume":"19","author":"Q Mei","year":"2020","unstructured":"Mei Q, G\u00fcl M (2020) Multi-level feature fusion in densely connected deep-learning architecture and depth-first search for crack segmentation on images collected with smartphones. Struct Health Monit 19(6):1726\u20131744. https:\/\/doi.org\/10.1177\/1475921719896813","journal-title":"Struct Health Monit"},{"issue":"9","key":"18568_CR16","doi-asserted-by":"publisher","first-page":"1896","DOI":"10.1177\/1369433220986637","volume":"24","author":"ADNA Andrushia","year":"2021","unstructured":"Andrushia ADNA, Lubloy E (2021) Deep learning based thermal crack detection on structural concrete exposed to elevated temperature. Adv Struct Eng 24(9):1896\u20131909. https:\/\/doi.org\/10.1177\/1369433220986637","journal-title":"Adv Struct Eng"},{"key":"18568_CR17","doi-asserted-by":"publisher","first-page":"04020038","DOI":"10.1061\/(ASCE)","volume":"34","author":"Y-A Hsieh","year":"2020","unstructured":"Hsieh Y-A, Tsai YJ (2020) Machine learning for crack detection: review and model performance comparison. J Comput Civ Eng 34:04020038. https:\/\/doi.org\/10.1061\/(ASCE)","journal-title":"J Comput Civ Eng"},{"key":"18568_CR18","doi-asserted-by":"publisher","unstructured":"Krizhevsky A, Sutskever I, Hinton G (2012) Imagenet classification with deep convolutional neural networks[J]. Adv Neural Inf Process Syst 25(2). https:\/\/doi.org\/10.1145\/3065386","DOI":"10.1145\/3065386"},{"key":"18568_CR19","doi-asserted-by":"publisher","unstructured":"Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition[J]. Comput Sci. https:\/\/doi.org\/10.48550\/arXiv.1409.1556","DOI":"10.48550\/arXiv.1409.1556"},{"key":"18568_CR20","doi-asserted-by":"publisher","unstructured":"Szegedy C, Liu W, Jia Y et al (2015) Going deeper with convolutions[J]. 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). https:\/\/doi.org\/10.1109\/CVPR.2015.7298594","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"18568_CR21","doi-asserted-by":"publisher","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). https:\/\/doi.org\/10.1109\/CVPR.2016.90","DOI":"10.1109\/CVPR.2016.90"},{"issue":"5","key":"18568_CR22","doi-asserted-by":"publisher","first-page":"361","DOI":"10.1111\/mice.12263","volume":"32","author":"Y-J Cha","year":"2017","unstructured":"Cha Y-J, Choi W, B\u00fcy\u00fck\u00f6zt\u00fcrk O (2017) Deep Learning-Based Crack Damage Detection Using Convolutional Neural Networks. Comput-Aided Civ Infrastruct Eng 32(5):361\u2013378. https:\/\/doi.org\/10.1111\/mice.12263","journal-title":"Comput-Aided Civ Infrastruct Eng"},{"issue":"5","key":"18568_CR23","doi-asserted-by":"publisher","first-page":"367","DOI":"10.1111\/mice.12421","volume":"34","author":"F Ni","year":"2018","unstructured":"Ni F, Zhang J, Chen Z (2018) Zernike-moment measurement of thin-crack width in images enabled by dual-scale deep learning. Comput-Aided Civ Infrastruct Eng 34(5):367\u2013384. https:\/\/doi.org\/10.1111\/mice.12421","journal-title":"Comput-Aided Civ Infrastruct Eng"},{"issue":"6","key":"18568_CR24","doi-asserted-by":"publisher","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","volume":"39","author":"S Ren","year":"2017","unstructured":"Ren S, He K, Girshick R, Sun J (2017) Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks. IEEE Trans Pattern Anal Mach Intell 39(6):1137\u20131149. https:\/\/doi.org\/10.1109\/TPAMI.2016.2577031","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"18568_CR25","doi-asserted-by":"publisher","unstructured":"Redmon J, Divvala S, Girshick R, Farhadi A (2016) You only look once: unified, real-time object detection. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). https:\/\/doi.org\/10.1109\/CVPR.2016.91","DOI":"10.1109\/CVPR.2016.91"},{"key":"18568_CR26","doi-asserted-by":"publisher","unstructured":"Liu W, Anguelov D, Erhan D, Szegedy C, Reed S, Fu C-Y, Berg AC (2016) SSD: single shot multibox detector. Computer Vision \u2013 ECCV 2016. https:\/\/doi.org\/10.1007\/978-3-319-46448-0_2","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"18568_CR27","doi-asserted-by":"publisher","first-page":"120","DOI":"10.1016\/j.eswa.2023.120200","volume":"227","author":"AN Tabata","year":"2023","unstructured":"Tabata AN, Zimmer A, dos Santos Coelho L, Mariani VC (2023) Analyzing CARLA \u2019s performance for 2D object detection and monocular depth estimation based on deep learning approaches. Expert Syst Appl 227:120. https:\/\/doi.org\/10.1016\/j.eswa.2023.120200","journal-title":"Expert Syst Appl"},{"key":"18568_CR28","doi-asserted-by":"publisher","unstructured":"Ouali I, Ben Halima M, Wali A (2022) Real-time application for recognition and visualization of arabic words with vowels based DL and AR. 2022 International Wireless Communications and Mobile Computing (IWCMC), pp 678\u2013683. https:\/\/doi.org\/10.1109\/IWCMC55113.2022.9825089","DOI":"10.1109\/IWCMC55113.2022.9825089"},{"key":"18568_CR29","doi-asserted-by":"publisher","first-page":"107531","DOI":"10.1016\/j.mtcomm.2023.107531","volume":"37","author":"L Che","year":"2023","unstructured":"Che L, He Z, Zheng K, Si T, Ge M, Cheng H, Zeng L (2023) Deep learning in alloy material microstructures: Application and prospects. Mater Today Commun 37:107531. https:\/\/doi.org\/10.1016\/j.mtcomm.2023.107531","journal-title":"Mater Today Commun"},{"issue":"12","key":"18568_CR30","doi-asserted-by":"publisher","first-page":"1090","DOI":"10.1111\/mice.12412","volume":"33","author":"X Yang","year":"2018","unstructured":"Yang X, Li H, Yu Y, Luo X, Huang T, Yang X (2018) Automatic Pixel-Level Crack Detection and Measurement Using Fully Convolutional Network. Comput-Aided Civ Infrastruct Eng 33(12):1090\u20131109. https:\/\/doi.org\/10.1111\/mice.12412","journal-title":"Comput-Aided Civ Infrastruct Eng"},{"key":"18568_CR31","doi-asserted-by":"publisher","unstructured":"Ronneberger O, Fischer P, Brox T (2015) U-Net: convolutional networks for biomedical image segmentation. Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2015, pp 234\u2013241. https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28","DOI":"10.1007\/978-3-319-24574-4_28"},{"issue":"12","key":"18568_CR32","doi-asserted-by":"publisher","first-page":"2481","DOI":"10.1109\/TPAMI.2016.2644615","volume":"39","author":"V Badrinarayanan","year":"2017","unstructured":"Badrinarayanan V, Kendall A, Cipolla R (2017) SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation. IEEE Trans Pattern Anal Mach Intell 39(12):2481\u20132495. https:\/\/doi.org\/10.1109\/TPAMI.2016.2644615","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"18568_CR33","doi-asserted-by":"publisher","unstructured":"Chen L-C, Zhu Y, Papandreou G, Schro F, Adam H (2018) Encoder-decoder with atrous separable convolution for semantic image segmentation. Computer Vision \u2013 ECCV 2018, pp 833\u2013851. https:\/\/doi.org\/10.1007\/978-3-030-01234-2_49","DOI":"10.1007\/978-3-030-01234-2_49"},{"issue":"6","key":"18568_CR34","doi-asserted-by":"publisher","first-page":"2547","DOI":"10.1109\/TNNLS.2020.3006524","volume":"32","author":"J Fu","year":"2021","unstructured":"Fu J, Liu J, Jiang J, Li Y, Bao Y, Lu H (2021) Scene Segmentation With Dual Relation-Aware Attention Network. IEEE Trans Neural Netw Learn Syst 32(6):2547\u20132560. https:\/\/doi.org\/10.1109\/TNNLS.2020.3006524","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"18568_CR35","doi-asserted-by":"publisher","first-page":"130867","DOI":"10.1016\/j.matlet.2021.130867","volume":"306","author":"X Cui","year":"2022","unstructured":"Cui X, Wang Q, Dai J, Li S, Xie C, Wang J (2022) Pixel-level intelligent recognition of concrete cracks based on DRACNN. Mater Lett 306:130867. https:\/\/doi.org\/10.1016\/j.matlet.2021.130867","journal-title":"Mater Lett"},{"issue":"4","key":"18568_CR36","doi-asserted-by":"publisher","first-page":"4474","DOI":"10.1109\/tits.2023.3236247","volume":"24","author":"T Zhang","year":"2023","unstructured":"Zhang T, Wang D, Mullins A, Lu Y (2023) Integrated APC-GAN and AttuNet Framework for Automated Pavement Crack Pixel-Level Segmentation: A New Solution to Small Training Datasets. IEEE trans Intell Transp Syst 24(4):4474\u20134481. https:\/\/doi.org\/10.1109\/tits.2023.3236247","journal-title":"IEEE trans Intell Transp Syst"},{"key":"18568_CR37","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/lgrs.2021.3129607","volume":"19","author":"Z Hong","year":"2022","unstructured":"Hong Z, Yang F, Pan H, Zhou R, Zhang Y, Han Y, Wang J, Yang S, Chen P, Tong X, Liu J (2022) Highway Crack Segmentation From Unmanned Aerial Vehicle Images Using Deep Learning. IEEE Geosci Remote Sens Lett 19:1\u20135. https:\/\/doi.org\/10.1109\/lgrs.2021.3129607","journal-title":"IEEE Geosci Remote Sens Lett"},{"issue":"10","key":"18568_CR38","doi-asserted-by":"publisher","first-page":"1561","DOI":"10.3390\/buildings12101561","volume":"12","author":"H Su","year":"2022","unstructured":"Su H, Wang X, Han T, Wang Z, Zhao Z, Zhang P (2022) Research on a U-Net Bridge Crack Identification and Feature-Calculation Methods Based on a CBAM Attention Mechanism. Build 12(10):1561. https:\/\/doi.org\/10.3390\/buildings12101561","journal-title":"Build"},{"key":"18568_CR39","doi-asserted-by":"publisher","first-page":"117367","DOI":"10.1016\/j.conbuildmat.2019.117367","volume":"234","author":"Y Ren","year":"2020","unstructured":"Ren Y, Huang J, Hong Z, Lu W, Yin J, Zou L, Shen X (2020) Image-based concrete crack detection in tunnels using deep fully convolutional networks. Constr Build Mater 234:117367. https:\/\/doi.org\/10.1016\/j.conbuildmat.2019.117367","journal-title":"Constr Build Mater"},{"key":"18568_CR40","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2021\/6654996","volume":"2021","author":"W Qiao","year":"2021","unstructured":"Qiao W, Zhang H, Zhu F, Wu Q, Lonetti P (2021) A Crack Identification Method for Concrete Structures Using Improved U-Net Convolutional Neural Networks. Math Probl Eng 2021:1\u201316. https:\/\/doi.org\/10.1155\/2021\/6654996","journal-title":"Math Probl Eng"},{"issue":"6","key":"18568_CR41","doi-asserted-by":"publisher","first-page":"2330","DOI":"10.3390\/s22062330","volume":"22","author":"A Wang","year":"2022","unstructured":"Wang A, Togo R, Ogawa T, Haseyama M (2022) Defect Detection of Subway Tunnels Using Advanced U-Net Network. Sensors (Basel) 22(6):2330. https:\/\/doi.org\/10.3390\/s22062330","journal-title":"Sensors (Basel)"},{"key":"18568_CR42","doi-asserted-by":"publisher","first-page":"430","DOI":"10.1016\/j.istruc.2023.02.010","volume":"50","author":"H Cheng","year":"2023","unstructured":"Cheng H, Li Y, Li H, Hu Q (2023) Embankment crack detection in UAV images based on efficient channel attention U2Net. Structures 50:430\u2013443. https:\/\/doi.org\/10.1016\/j.istruc.2023.02.010","journal-title":"Structures"},{"key":"18568_CR43","doi-asserted-by":"publisher","first-page":"112475","DOI":"10.1016\/j.measurement.2023.112475","volume":"208","author":"J Shang","year":"2023","unstructured":"Shang J, Xu J, Zhang AA, Liu Y, Wang KCP, Ren D, Zhang H, Dong Z, He A (2023) Automatic Pixel-level pavement sealed crack detection using Multi-fusion U-Net network. Measurement 208:112475","journal-title":"Measurement"},{"key":"18568_CR44","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/lgrs.2022.3229556","volume":"20","author":"T Zhong","year":"2023","unstructured":"Zhong T, Wang W, Lu S, Dong X, Yang B (2023) RMCHN: A Residual Modular Cascaded Heterogeneous Network for Noise Suppression in DAS-VSP Records. IEEE Geosci Remote Sens Lett 20:1\u20135. https:\/\/doi.org\/10.1109\/lgrs.2022.3229556","journal-title":"IEEE Geosci Remote Sens Lett"},{"key":"18568_CR45","doi-asserted-by":"publisher","unstructured":"Woo S, Park J, Lee J-Y, Kweon IS (2018) CBAM: Convolutional Block Attention Module. Proceedings of the European conference on computer vision. Computer Vision \u2013 ECCV 2018, pp 3\u201319. https:\/\/doi.org\/10.1007\/978-3-030-01234-2_1","DOI":"10.1007\/978-3-030-01234-2_1"},{"issue":"9","key":"18568_CR46","doi-asserted-by":"publisher","first-page":"8016","DOI":"10.1109\/tie.2019.2945265","volume":"67","author":"W Choi","year":"2020","unstructured":"Choi W, Cha Y-J (2020) SDDNet: Real-Time Crack Segmentation. IEEE Trans Ind Electron 67(9):8016\u20138025. https:\/\/doi.org\/10.1109\/tie.2019.2945265","journal-title":"IEEE Trans Ind Electron"},{"issue":"4","key":"18568_CR47","doi-asserted-by":"publisher","first-page":"834","DOI":"10.1109\/TPAMI.2017.2699184","volume":"40","author":"LC Chen","year":"2018","unstructured":"Chen LC, Papandreou G, Kokkinos I, Murphy K, Yuille AL (2018) DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs. IEEE Trans Pattern Anal Mach Intell 40(4):834\u2013848. https:\/\/doi.org\/10.1109\/TPAMI.2017.2699184","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"9","key":"18568_CR48","doi-asserted-by":"publisher","first-page":"4714","DOI":"10.3390\/app12094714","volume":"12","author":"P Li","year":"2022","unstructured":"Li P, Xia H, Zhou B, Yan F, Guo R (2022) A Method to Improve the Accuracy of Pavement Crack Identification by Combining a Semantic Segmentation and Edge Detection Model. Appl Sci 12(9):4714. https:\/\/doi.org\/10.3390\/app12094714","journal-title":"Appl Sci"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-024-18568-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-024-18568-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-024-18568-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,3]],"date-time":"2024-09-03T08:31:05Z","timestamp":1725352265000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-024-18568-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,2,19]]},"references-count":48,"journal-issue":{"issue":"31","published-online":{"date-parts":[[2024,9]]}},"alternative-id":["18568"],"URL":"https:\/\/doi.org\/10.1007\/s11042-024-18568-3","relation":{},"ISSN":["1573-7721"],"issn-type":[{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,2,19]]},"assertion":[{"value":"9 December 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 January 2024","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 January 2024","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 February 2024","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in the manuscript.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interest"}}]}}